Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices
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Open Access
RESEARCH
Potential predictors of type‑2 diabetes risk: machine learning, synthetic data and wearable health devices Paola Stolfi1* , Ilaria Valentini2, Maria Concetta Palumbo1, Paolo Tieri1, Andrea Grignolio3,4 and Filippo Castiglione1
From 3rd International Workshop on Computational Methods for the Immune System Function (CMISF 2019) San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] 1 Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy Full list of author information is available at the end of the article
Abstract Background: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals. Results: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. Conclusions: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM. Keywords: Machine learning, Random forest, Emulator, T2D, Computational modeling, Synthetic data
Background Type 2 diabetes (i.e. non-insulin-dependent, T2D) is a chronic, multifactorial, metabolic disorder typical of late adulthood characterised by less effective hormone insulin efficiency at lowering blood sugar. The World Health Organization reports that type 2 diabetes accounts for 85–90% of all cases of diabetes in the World [1]. There are many different mechanisms that contribute to the onset of T2D [2], therefore research is focusing on the simultaneous observation of several factors such as © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the co
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